Improvement algorithm of dynamic Allan variance and its application in analysis of FOG start-up signal

Lixin, 汪立新 Wang; Zhanhui, 朱战辉 Zhu; Rui, 李 瑞 Li

doi:10.3788/irla201645.0726004

Cited by 1 publication

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“…So adjust the length of a rectangular window only based on kurtosis (brief account DAVAR-K-3.3) as the same as Wang’s method [12,13]. The kurtosis and the window length are shown in Figure 24.…”

Section: Simulation and Testmentioning

confidence: 99%

“…And next, the signals with different frequencies were used different window functions to intercept the original signal, which reduce the power loss of DAVAR method [11]. Zhu et al adjust the length of the rectangular window according to the original signal kurtosis and the changes of variance to improve the confidence level of the variance estimation effectively [12,13]. But the kurtosis and variance reflect the stability of the rectangular window interception signal, so when the length of the window is changed according to the variance and kurtosis, there are defects in the lag of window length regulation.…”

Section: Introductionmentioning

confidence: 99%

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In view that traditional dynamic Allan variance (DAVAR) method is difficult to make a good balance between dynamic tracking capabilities and the confidence of the estimation. And the reason is the use of a rectangular window with the fixed window length to intercept the original signal. So an improved dynamic Allan variance method was proposed. Compared with the traditional Allan variance method, this method can adjust the window length of the rectangular window adaptively. The data in the beginning and terminal interval was extended with the inverted mirror extension method to improve the utilization rate of the interval data. And the sliding kurtosis contribution coefficient and kurtosis were introduced to adjust the length of the rectangular window by sensing the content of shock signal in terminal interval. The method analyzed the window length change factor in different stable conditions and adjusted the rectangular window’s window length according to the kurtosis, sliding kurtosis contribution coefficient. The test results show that the more the kurtosis stability threshold was close to 3, the stronger the dynamic tracking ability of DAVAR would be. But the kurtosis stability threshold was too close to 3, there was a misjudgement in kurtosis analysis of signal stability, resulting in distortion of DAVAR analysis results. When using the improved DAVAR method, the kurtosis stability threshold can be close to 3 to improve the tracking ability and the estimation confidence in stable interval. Therefore, it solved the problem that the dynamic Allan variance tracking ability and confidence level were difficult to take into account, and also solved the problem of misjudgement in the stability analysis of kurtosis.

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Section: Simulation and Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%